{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) 2020, NVIDIA CORPORATION.\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "you may not use this file except in compliance with the License.\n",
    "You may obtain a copy of the License at\n",
    "    http://www.apache.org/licenses/LICENSE-2.0\n",
    "Unless required by applicable law or agreed to in writing, software\n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "See the License for the specific language governing permissions and\n",
    "limitations under the License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, time\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "start = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.14.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd, numpy as np, gc\n",
    "from datetime import datetime\n",
    "import joblib, time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.max_rows', 500)\n",
    "import cudf, cupy, time\n",
    "cudf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "startNB = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numba import jit, njit, prange\n",
    "from sklearn.metrics import precision_recall_curve, auc, log_loss\n",
    "\n",
    "def compute_prauc(gt, pred, nafill=True):\n",
    "    if nafill:\n",
    "        pred[ np.isnan(pred) ] = np.nanmean( pred )\n",
    "    prec, recall, thresh = precision_recall_curve(gt, pred)\n",
    "    prauc = auc(recall, prec)\n",
    "    return prauc\n",
    "\n",
    "@jit\n",
    "def fast_auc(y_true, y_prob):\n",
    "    y_true = np.asarray(y_true)\n",
    "    y_true = y_true[np.argsort(y_prob)]\n",
    "    nfalse = 0\n",
    "    auc = 0\n",
    "    n = len(y_true)\n",
    "    for i in range(n):\n",
    "        y_i = y_true[i]\n",
    "        nfalse += (1 - y_i)\n",
    "        auc += y_i * nfalse\n",
    "    auc /= (nfalse * (n - nfalse))\n",
    "    return auc\n",
    "\n",
    "@njit\n",
    "def numba_log_loss(y,x):\n",
    "    n = x.shape[0]\n",
    "    ll = 0.\n",
    "    for i in prange(n):\n",
    "        if y[i]<=0.:\n",
    "            ll += np.log(1-x[i] + 1e-15 )\n",
    "        else:\n",
    "            ll += np.log(x[i] + 1e-15)\n",
    "    return -ll / n\n",
    "\n",
    "def compute_rce(gt , pred, nafill=True, verbose=0):\n",
    "    if nafill:\n",
    "        pred[ np.isnan(pred) ] = np.nanmean( pred )\n",
    "        \n",
    "    cross_entropy = numba_log_loss( gt, pred  )\n",
    "    \n",
    "    yt = np.mean(gt>0)     \n",
    "    strawman_cross_entropy = -(yt*np.log(yt) + (1 - yt)*np.log(1 - yt))\n",
    "    \n",
    "    if verbose:\n",
    "        print( \"logloss: {0:.5f} / {1:.5f} = {2:.5f}\".format(cross_entropy, strawman_cross_entropy, cross_entropy/strawman_cross_entropy))\n",
    "        print( 'mean:    {0:.5f} / {1:.5f}'.format( np.nanmean( pred ) , yt  ) )\n",
    "    \n",
    "    return (1.0 - cross_entropy/strawman_cross_entropy)*100.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_memory( df ):\n",
    "    features = df.columns\n",
    "    for i in range( df.shape[1] ):\n",
    "        if df.dtypes[i] == 'uint8':\n",
    "            df[features[i]] = df[features[i]].astype( np.int8 )\n",
    "            gc.collect()\n",
    "        elif df.dtypes[i] == 'bool':\n",
    "            df[features[i]] = df[features[i]].astype( np.int8 )\n",
    "            gc.collect()\n",
    "        elif df.dtypes[i] == 'uint32':\n",
    "            df[features[i]] = df[features[i]].astype( np.int32 )\n",
    "            gc.collect()\n",
    "        elif df.dtypes[i] == 'int64':\n",
    "            df[features[i]] = df[features[i]].astype( np.int32 )\n",
    "            gc.collect()\n",
    "        elif df.dtypes[i] == 'float64':\n",
    "            df[features[i]] = df[features[i]].astype( np.float32 )\n",
    "            gc.collect()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TARGET_id = 2\n",
    "\n",
    "TARGETS = ['reply', 'retweet', 'retweet_comment', 'like']\n",
    "\n",
    "TARGET = TARGETS[TARGET_id]\n",
    "\n",
    "train = pd.read_parquet( 'data/train-final-te-'+TARGET+'-1.parquet' )\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['a_follower_count']  = train.groupby('a_user_id')['a_follower_count' ].transform('last');_ = gc.collect()\n",
    "train['a_following_count'] = train.groupby('a_user_id')['a_following_count'].transform('last');_ = gc.collect()\n",
    "train['b_follower_count']  = train.groupby('b_user_id')['a_follower_count' ].transform('last');_ = gc.collect()\n",
    "train['b_following_count'] = train.groupby('b_user_id')['a_following_count'].transform('last');_ = gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hashtags</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>media</th>\n",
       "      <th>links</th>\n",
       "      <th>domains</th>\n",
       "      <th>tweet_type</th>\n",
       "      <th>language</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>a_user_id</th>\n",
       "      <th>a_follower_count</th>\n",
       "      <th>a_following_count</th>\n",
       "      <th>a_is_verified</th>\n",
       "      <th>a_account_creation</th>\n",
       "      <th>b_user_id</th>\n",
       "      <th>b_follower_count</th>\n",
       "      <th>b_following_count</th>\n",
       "      <th>b_is_verified</th>\n",
       "      <th>b_account_creation</th>\n",
       "      <th>b_follows_a</th>\n",
       "      <th>reply</th>\n",
       "      <th>retweet</th>\n",
       "      <th>retweet_comment</th>\n",
       "      <th>like</th>\n",
       "      <th>id</th>\n",
       "      <th>tr</th>\n",
       "      <th>dt_day</th>\n",
       "      <th>dt_dow</th>\n",
       "      <th>dt_hour</th>\n",
       "      <th>a_count_combined</th>\n",
       "      <th>a_user_fer_count_delta_time</th>\n",
       "      <th>a_user_fing_count_delta_time</th>\n",
       "      <th>a_user_fering_count_delta_time</th>\n",
       "      <th>a_user_fing_count_mode</th>\n",
       "      <th>a_user_fer_count_mode</th>\n",
       "      <th>a_user_fering_count_mode</th>\n",
       "      <th>count_ats</th>\n",
       "      <th>count_char</th>\n",
       "      <th>count_words</th>\n",
       "      <th>tw_hash</th>\n",
       "      <th>tw_freq_hash</th>\n",
       "      <th>tw_first_word</th>\n",
       "      <th>tw_second_word</th>\n",
       "      <th>tw_last_word</th>\n",
       "      <th>tw_llast_word</th>\n",
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       "      <th>tw_rt_uhash</th>\n",
       "      <th>TE_b_user_id_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_first_word_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_last_word_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_hash0_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_hash1_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_rt_uhash_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_a_user_id_retweet_comment</th>\n",
       "      <th>TE_b_user_id_retweet_comment</th>\n",
       "      <th>TE_tw_hash_retweet_comment</th>\n",
       "      <th>TE_tw_freq_hash_retweet_comment</th>\n",
       "      <th>TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet_comment</th>\n",
       "      <th>TE_a_count_combined_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fer_count_delta_time_media_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fing_count_delta_time_media_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fering_count_delta_time_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fing_count_mode_media_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fer_count_mode_media_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fering_count_mode_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_domains_media_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_links_media_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_hashtags_media_tweet_type_language_retweet_comment</th>\n",
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       "      <td>0</td>\n",
       "      <td>161</td>\n",
       "      <td>42</td>\n",
       "      <td>50323632</td>\n",
       "      <td>44880036</td>\n",
       "      <td>7551233</td>\n",
       "      <td>985413</td>\n",
       "      <td>112</td>\n",
       "      <td>2712</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002388</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005743</td>\n",
       "      <td>0.003372</td>\n",
       "      <td>0.003387</td>\n",
       "      <td>0.003372</td>\n",
       "      <td>0.001995</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002645</td>\n",
       "      <td>0.005460</td>\n",
       "      <td>0.006452</td>\n",
       "      <td>0.004916</td>\n",
       "      <td>0.005489</td>\n",
       "      <td>0.004650</td>\n",
       "      <td>0.006107</td>\n",
       "      <td>0.005307</td>\n",
       "      <td>0.002163</td>\n",
       "      <td>0.002163</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146256003</th>\n",
       "      <td>6845</td>\n",
       "      <td>73443138</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>1581799075</td>\n",
       "      <td>6937005</td>\n",
       "      <td>4354</td>\n",
       "      <td>3629</td>\n",
       "      <td>0</td>\n",
       "      <td>1269050894</td>\n",
       "      <td>3141261</td>\n",
       "      <td>4354</td>\n",
       "      <td>3629</td>\n",
       "      <td>0</td>\n",
       "      <td>1501554925</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>146256003</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>216</td>\n",
       "      <td>71</td>\n",
       "      <td>50323633</td>\n",
       "      <td>44880037</td>\n",
       "      <td>2674107</td>\n",
       "      <td>2769099</td>\n",
       "      <td>646</td>\n",
       "      <td>2378</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002388</td>\n",
       "      <td>0.005023</td>\n",
       "      <td>0.003427</td>\n",
       "      <td>0.003372</td>\n",
       "      <td>0.003387</td>\n",
       "      <td>0.003372</td>\n",
       "      <td>0.005827</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.003437</td>\n",
       "      <td>0.005460</td>\n",
       "      <td>0.005522</td>\n",
       "      <td>0.004699</td>\n",
       "      <td>0.005489</td>\n",
       "      <td>0.004540</td>\n",
       "      <td>0.005375</td>\n",
       "      <td>0.005307</td>\n",
       "      <td>0.003575</td>\n",
       "      <td>0.003575</td>\n",
       "      <td>0.000089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hashtags  tweet_id  media  links  domains  tweet_type  language  \\\n",
       "146255999         0  73443134      0      0        0           1        59   \n",
       "146256000         0  73443135      0      0        0           1        54   \n",
       "146256001         0  73443136      9      0        0           1         4   \n",
       "146256002    845560  73443137      0      0        0           2        11   \n",
       "146256003      6845  73443138      7      0        0           2        11   \n",
       "\n",
       "            timestamp  a_user_id  a_follower_count  a_following_count  \\\n",
       "146255999  1582081341    1362744              5390                833   \n",
       "146256000  1582091134     179475             17747               1467   \n",
       "146256001  1582086464     366281              4386                 80   \n",
       "146256002  1581665518    1030299              4236               4119   \n",
       "146256003  1581799075    6937005              4354               3629   \n",
       "\n",
       "           a_is_verified  a_account_creation  b_user_id  b_follower_count  \\\n",
       "146255999              0          1546658592   30848700              5390   \n",
       "146256000              0          1172558525    4950585             17747   \n",
       "146256001              0          1457176980   30312854              4386   \n",
       "146256002              0          1524226898    3141261              4354   \n",
       "146256003              0          1269050894    3141261              4354   \n",
       "\n",
       "           b_following_count  b_is_verified  b_account_creation  b_follows_a  \\\n",
       "146255999                833              0          1428007228            0   \n",
       "146256000               1467              0          1272381192            0   \n",
       "146256001                 80              0          1235182992            0   \n",
       "146256002               3629              0          1501554925            1   \n",
       "146256003               3629              0          1501554925            1   \n",
       "\n",
       "           reply  retweet  retweet_comment  like         id  tr  dt_day  \\\n",
       "146255999      0        0                0     0  146255999   2      19   \n",
       "146256000      0        0                0     0  146256000   2      19   \n",
       "146256001      0        0                0     0  146256001   2      19   \n",
       "146256002      0        0                0     0  146256002   2      14   \n",
       "146256003      0        0                0     0  146256003   2      15   \n",
       "\n",
       "           dt_dow  dt_hour  a_count_combined  a_user_fer_count_delta_time  \\\n",
       "146255999       2        3                 2                            1   \n",
       "146256000       2        5                 0                            0   \n",
       "146256001       2        4                 2                            1   \n",
       "146256002       4        7                 2                            1   \n",
       "146256003       5       20                 2                            1   \n",
       "\n",
       "           a_user_fing_count_delta_time  a_user_fering_count_delta_time  \\\n",
       "146255999                             1                               1   \n",
       "146256000                             1                               0   \n",
       "146256001                             1                               1   \n",
       "146256002                             1                               1   \n",
       "146256003                             1                               1   \n",
       "\n",
       "           a_user_fing_count_mode  a_user_fer_count_mode  \\\n",
       "146255999                       1                      1   \n",
       "146256000                       1                      0   \n",
       "146256001                       1                      1   \n",
       "146256002                       1                      1   \n",
       "146256003                       1                      1   \n",
       "\n",
       "           a_user_fering_count_mode  count_ats  count_char  count_words  \\\n",
       "146255999                         1          0          76           15   \n",
       "146256000                         0          0         159           29   \n",
       "146256001                         1          0          72           10   \n",
       "146256002                         1          0         161           42   \n",
       "146256003                         1          0         216           71   \n",
       "\n",
       "            tw_hash  tw_freq_hash  tw_first_word  tw_second_word  \\\n",
       "146255999  39170347      34858164          28651          210319   \n",
       "146256000  38847520       1535005         300662          267542   \n",
       "146256001  49748666      44369960        7361311          466019   \n",
       "146256002  50323632      44880036        7551233          985413   \n",
       "146256003  50323633      44880037        2674107         2769099   \n",
       "\n",
       "           tw_last_word  tw_llast_word  tw_len  tw_hash0  tw_hash1  \\\n",
       "146255999          2793          27536       6         0         0   \n",
       "146256000            11            867      13         0         0   \n",
       "146256001         19261         202536       5         0         0   \n",
       "146256002           112           2712      14         0         0   \n",
       "146256003           646           2378      26         0         0   \n",
       "\n",
       "           tw_rt_uhash  TE_b_user_id_tweet_type_language_retweet_comment  \\\n",
       "146255999         7739                                               NaN   \n",
       "146256000        10748                                          0.006621   \n",
       "146256001        58462                                               NaN   \n",
       "146256002            0                                          0.002388   \n",
       "146256003            0                                          0.002388   \n",
       "\n",
       "           TE_tw_first_word_tweet_type_language_retweet_comment  \\\n",
       "146255999                                           0.014166      \n",
       "146256000                                           0.009727      \n",
       "146256001                                           0.006936      \n",
       "146256002                                                NaN      \n",
       "146256003                                           0.005023      \n",
       "\n",
       "           TE_tw_last_word_tweet_type_language_retweet_comment  \\\n",
       "146255999                                           0.012681     \n",
       "146256000                                           0.005502     \n",
       "146256001                                           0.000225     \n",
       "146256002                                           0.005743     \n",
       "146256003                                           0.003427     \n",
       "\n",
       "           TE_tw_hash0_tweet_type_language_retweet_comment  \\\n",
       "146255999                                         0.010811   \n",
       "146256000                                         0.007278   \n",
       "146256001                                         0.006057   \n",
       "146256002                                         0.003372   \n",
       "146256003                                         0.003372   \n",
       "\n",
       "           TE_tw_hash1_tweet_type_language_retweet_comment  \\\n",
       "146255999                                         0.010302   \n",
       "146256000                                         0.006833   \n",
       "146256001                                         0.005832   \n",
       "146256002                                         0.003387   \n",
       "146256003                                         0.003387   \n",
       "\n",
       "           TE_tw_rt_uhash_tweet_type_language_retweet_comment  \\\n",
       "146255999                                           0.013181    \n",
       "146256000                                           0.008766    \n",
       "146256001                                           0.000792    \n",
       "146256002                                           0.003372    \n",
       "146256003                                           0.003372    \n",
       "\n",
       "           TE_a_user_id_retweet_comment  TE_b_user_id_retweet_comment  \\\n",
       "146255999                      0.002697                           NaN   \n",
       "146256000                      0.001267                      0.004855   \n",
       "146256001                      0.004092                           NaN   \n",
       "146256002                      0.001995                      0.002081   \n",
       "146256003                      0.005827                      0.002081   \n",
       "\n",
       "           TE_tw_hash_retweet_comment  TE_tw_freq_hash_retweet_comment  \\\n",
       "146255999                         NaN                              NaN   \n",
       "146256000                         NaN                         0.006936   \n",
       "146256001                         NaN                              NaN   \n",
       "146256002                         NaN                              NaN   \n",
       "146256003                         NaN                              NaN   \n",
       "\n",
       "           TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet_comment  \\\n",
       "146255999                                           0.006404                                      \n",
       "146256000                                           0.005237                                      \n",
       "146256001                                           0.005116                                      \n",
       "146256002                                           0.002645                                      \n",
       "146256003                                           0.003437                                      \n",
       "\n",
       "           TE_a_count_combined_tweet_type_language_retweet_comment  \\\n",
       "146255999                                           0.023603         \n",
       "146256000                                           0.000053         \n",
       "146256001                                           0.011182         \n",
       "146256002                                           0.005460         \n",
       "146256003                                           0.005460         \n",
       "\n",
       "           TE_a_user_fer_count_delta_time_media_language_retweet_comment  \\\n",
       "146255999                                           0.017441               \n",
       "146256000                                           0.000177               \n",
       "146256001                                           0.009863               \n",
       "146256002                                           0.006452               \n",
       "146256003                                           0.005522               \n",
       "\n",
       "           TE_a_user_fing_count_delta_time_media_language_retweet_comment  \\\n",
       "146255999                                           0.014932                \n",
       "146256000                                           0.011367                \n",
       "146256001                                           0.008235                \n",
       "146256002                                           0.004916                \n",
       "146256003                                           0.004699                \n",
       "\n",
       "           TE_a_user_fering_count_delta_time_tweet_type_language_retweet_comment  \\\n",
       "146255999                                           0.023445                       \n",
       "146256000                                           0.000117                       \n",
       "146256001                                           0.011110                       \n",
       "146256002                                           0.005489                       \n",
       "146256003                                           0.005489                       \n",
       "\n",
       "           TE_a_user_fing_count_mode_media_language_retweet_comment  \\\n",
       "146255999                                           0.014341          \n",
       "146256000                                           0.010685          \n",
       "146256001                                           0.008121          \n",
       "146256002                                           0.004650          \n",
       "146256003                                           0.004540          \n",
       "\n",
       "           TE_a_user_fer_count_mode_media_language_retweet_comment  \\\n",
       "146255999                                           0.016752         \n",
       "146256000                                           0.000330         \n",
       "146256001                                           0.009712         \n",
       "146256002                                           0.006107         \n",
       "146256003                                           0.005375         \n",
       "\n",
       "           TE_a_user_fering_count_mode_tweet_type_language_retweet_comment  \\\n",
       "146255999                                           0.021614                 \n",
       "146256000                                           0.000245                 \n",
       "146256001                                           0.010607                 \n",
       "146256002                                           0.005307                 \n",
       "146256003                                           0.005307                 \n",
       "\n",
       "           TE_domains_media_tweet_type_language_retweet_comment  \\\n",
       "146255999                                           0.008811      \n",
       "146256000                                           0.006263      \n",
       "146256001                                           0.006457      \n",
       "146256002                                           0.002163      \n",
       "146256003                                           0.003575      \n",
       "\n",
       "           TE_links_media_tweet_type_language_retweet_comment  \\\n",
       "146255999                                           0.008811    \n",
       "146256000                                           0.006263    \n",
       "146256001                                           0.006457    \n",
       "146256002                                           0.002163    \n",
       "146256003                                           0.003575    \n",
       "\n",
       "           TE_hashtags_media_tweet_type_language_retweet_comment  \n",
       "146255999                                           0.008885      \n",
       "146256000                                           0.006419      \n",
       "146256001                                           0.006883      \n",
       "146256002                                                NaN      \n",
       "146256003                                           0.000089      "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hashtags                                                                                  int32\n",
       "tweet_id                                                                                  int32\n",
       "media                                                                                      int8\n",
       "links                                                                                     int32\n",
       "domains                                                                                   int32\n",
       "tweet_type                                                                                 int8\n",
       "language                                                                                   int8\n",
       "timestamp                                                                                 int32\n",
       "a_user_id                                                                                 int32\n",
       "a_follower_count                                                                          int32\n",
       "a_following_count                                                                         int32\n",
       "a_is_verified                                                                              int8\n",
       "a_account_creation                                                                        int32\n",
       "b_user_id                                                                                 int32\n",
       "b_follower_count                                                                          int32\n",
       "b_following_count                                                                         int32\n",
       "b_is_verified                                                                              int8\n",
       "b_account_creation                                                                        int32\n",
       "b_follows_a                                                                                int8\n",
       "reply                                                                                     int32\n",
       "retweet                                                                                   int32\n",
       "retweet_comment                                                                           int32\n",
       "like                                                                                      int32\n",
       "id                                                                                        int32\n",
       "tr                                                                                        int32\n",
       "dt_day                                                                                     int8\n",
       "dt_dow                                                                                     int8\n",
       "dt_hour                                                                                    int8\n",
       "a_count_combined                                                                          int32\n",
       "a_user_fer_count_delta_time                                                               int32\n",
       "a_user_fing_count_delta_time                                                              int32\n",
       "a_user_fering_count_delta_time                                                            int32\n",
       "a_user_fing_count_mode                                                                    int32\n",
       "a_user_fer_count_mode                                                                     int32\n",
       "a_user_fering_count_mode                                                                  int32\n",
       "count_ats                                                                                 int32\n",
       "count_char                                                                                int32\n",
       "count_words                                                                               int32\n",
       "tw_hash                                                                                   int32\n",
       "tw_freq_hash                                                                              int32\n",
       "tw_first_word                                                                             int32\n",
       "tw_second_word                                                                            int32\n",
       "tw_last_word                                                                              int32\n",
       "tw_llast_word                                                                             int32\n",
       "tw_len                                                                                    int32\n",
       "tw_hash0                                                                                  int32\n",
       "tw_hash1                                                                                  int32\n",
       "tw_rt_uhash                                                                               int32\n",
       "TE_b_user_id_tweet_type_language_retweet_comment                                        float32\n",
       "TE_tw_first_word_tweet_type_language_retweet_comment                                    float32\n",
       "TE_tw_last_word_tweet_type_language_retweet_comment                                     float32\n",
       "TE_tw_hash0_tweet_type_language_retweet_comment                                         float32\n",
       "TE_tw_hash1_tweet_type_language_retweet_comment                                         float32\n",
       "TE_tw_rt_uhash_tweet_type_language_retweet_comment                                      float32\n",
       "TE_a_user_id_retweet_comment                                                            float32\n",
       "TE_b_user_id_retweet_comment                                                            float32\n",
       "TE_tw_hash_retweet_comment                                                              float32\n",
       "TE_tw_freq_hash_retweet_comment                                                         float32\n",
       "TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet_comment    float32\n",
       "TE_a_count_combined_tweet_type_language_retweet_comment                                 float32\n",
       "TE_a_user_fer_count_delta_time_media_language_retweet_comment                           float32\n",
       "TE_a_user_fing_count_delta_time_media_language_retweet_comment                          float32\n",
       "TE_a_user_fering_count_delta_time_tweet_type_language_retweet_comment                   float32\n",
       "TE_a_user_fing_count_mode_media_language_retweet_comment                                float32\n",
       "TE_a_user_fer_count_mode_media_language_retweet_comment                                 float32\n",
       "TE_a_user_fering_count_mode_tweet_type_language_retweet_comment                         float32\n",
       "TE_domains_media_tweet_type_language_retweet_comment                                    float32\n",
       "TE_links_media_tweet_type_language_retweet_comment                                      float32\n",
       "TE_hashtags_media_tweet_type_language_retweet_comment                                   float32\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_memory( train )\n",
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 35 features:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['media', 'tweet_type', 'a_follower_count', 'a_following_count',\n",
       "       'a_is_verified', 'b_follower_count', 'b_following_count',\n",
       "       'b_follows_a', 'dt_dow', 'dt_hour', 'count_ats', 'count_char',\n",
       "       'count_words', 'tw_len',\n",
       "       'TE_b_user_id_tweet_type_language_retweet_comment',\n",
       "       'TE_tw_first_word_tweet_type_language_retweet_comment',\n",
       "       'TE_tw_last_word_tweet_type_language_retweet_comment',\n",
       "       'TE_tw_hash0_tweet_type_language_retweet_comment',\n",
       "       'TE_tw_hash1_tweet_type_language_retweet_comment',\n",
       "       'TE_tw_rt_uhash_tweet_type_language_retweet_comment',\n",
       "       'TE_a_user_id_retweet_comment', 'TE_b_user_id_retweet_comment',\n",
       "       'TE_tw_hash_retweet_comment', 'TE_tw_freq_hash_retweet_comment',\n",
       "       'TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet_comment',\n",
       "       'TE_a_count_combined_tweet_type_language_retweet_comment',\n",
       "       'TE_a_user_fer_count_delta_time_media_language_retweet_comment',\n",
       "       'TE_a_user_fing_count_delta_time_media_language_retweet_comment',\n",
       "       'TE_a_user_fering_count_delta_time_tweet_type_language_retweet_comment',\n",
       "       'TE_a_user_fing_count_mode_media_language_retweet_comment',\n",
       "       'TE_a_user_fer_count_mode_media_language_retweet_comment',\n",
       "       'TE_a_user_fering_count_mode_tweet_type_language_retweet_comment',\n",
       "       'TE_domains_media_tweet_type_language_retweet_comment',\n",
       "       'TE_links_media_tweet_type_language_retweet_comment',\n",
       "       'TE_hashtags_media_tweet_type_language_retweet_comment'],\n",
       "      dtype='<U84')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_names = ['reply', 'retweet', 'retweet_comment', 'like']\n",
    "DONT_USE = ['timestamp','a_account_creation','b_account_creation','engage_time',\n",
    "            'a_account_creation', 'b_account_creation',\n",
    "            'fold','tweet_id', \n",
    "            'tr','dt_day','','',\n",
    "            'b_user_id','a_user_id','b_is_verified',\n",
    "            'elapsed_time',\n",
    "            'links','domains','hashtags0','hashtags1',\n",
    "            'hashtags','tweet_hash','dt_second','id',\n",
    "            'tw_hash0',\n",
    "            'tw_hash1',\n",
    "            'tw_rt_uhash',\n",
    "            'same_language', 'nan_language','language',\n",
    "            'tw_hash', 'tw_freq_hash','tw_first_word', 'tw_second_word', 'tw_last_word', 'tw_llast_word',\n",
    "            'ypred','a_count_combined','a_user_fer_count_delta_time','a_user_fing_count_delta_time','a_user_fering_count_delta_time','a_user_fing_count_mode','a_user_fer_count_mode','a_user_fering_count_mode'\n",
    "            \n",
    "           ]\n",
    "DONT_USE += label_names\n",
    "features = [c for c in train.columns if c not in DONT_USE]\n",
    "\n",
    "print('Using %i features:'%(len(features)))\n",
    "np.asarray(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hashtags</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>media</th>\n",
       "      <th>links</th>\n",
       "      <th>domains</th>\n",
       "      <th>tweet_type</th>\n",
       "      <th>language</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>a_user_id</th>\n",
       "      <th>a_follower_count</th>\n",
       "      <th>a_following_count</th>\n",
       "      <th>a_is_verified</th>\n",
       "      <th>a_account_creation</th>\n",
       "      <th>b_user_id</th>\n",
       "      <th>b_follower_count</th>\n",
       "      <th>b_following_count</th>\n",
       "      <th>b_is_verified</th>\n",
       "      <th>b_account_creation</th>\n",
       "      <th>b_follows_a</th>\n",
       "      <th>reply</th>\n",
       "      <th>retweet</th>\n",
       "      <th>retweet_comment</th>\n",
       "      <th>like</th>\n",
       "      <th>id</th>\n",
       "      <th>tr</th>\n",
       "      <th>dt_day</th>\n",
       "      <th>dt_dow</th>\n",
       "      <th>dt_hour</th>\n",
       "      <th>a_count_combined</th>\n",
       "      <th>a_user_fer_count_delta_time</th>\n",
       "      <th>a_user_fing_count_delta_time</th>\n",
       "      <th>a_user_fering_count_delta_time</th>\n",
       "      <th>a_user_fing_count_mode</th>\n",
       "      <th>a_user_fer_count_mode</th>\n",
       "      <th>a_user_fering_count_mode</th>\n",
       "      <th>count_ats</th>\n",
       "      <th>count_char</th>\n",
       "      <th>count_words</th>\n",
       "      <th>tw_hash</th>\n",
       "      <th>tw_freq_hash</th>\n",
       "      <th>tw_first_word</th>\n",
       "      <th>tw_second_word</th>\n",
       "      <th>tw_last_word</th>\n",
       "      <th>tw_llast_word</th>\n",
       "      <th>tw_len</th>\n",
       "      <th>tw_hash0</th>\n",
       "      <th>tw_hash1</th>\n",
       "      <th>tw_rt_uhash</th>\n",
       "      <th>TE_b_user_id_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_first_word_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_last_word_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_hash0_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_hash1_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_rt_uhash_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_a_user_id_retweet_comment</th>\n",
       "      <th>TE_b_user_id_retweet_comment</th>\n",
       "      <th>TE_tw_hash_retweet_comment</th>\n",
       "      <th>TE_tw_freq_hash_retweet_comment</th>\n",
       "      <th>TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet_comment</th>\n",
       "      <th>TE_a_count_combined_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fer_count_delta_time_media_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fing_count_delta_time_media_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fering_count_delta_time_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fing_count_mode_media_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fer_count_mode_media_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fering_count_mode_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_domains_media_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_links_media_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_hashtags_media_tweet_type_language_retweet_comment</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hashtags  tweet_id  media  links  domains  tweet_type  language  \\\n",
       "146255999         0  73443134      0      0        0           1        59   \n",
       "146256000         0  73443135      0      0        0           1        54   \n",
       "146256001         0  73443136      9      0        0           1         4   \n",
       "146256002    845560  73443137      0      0        0           2        11   \n",
       "146256003      6845  73443138      7      0        0           2        11   \n",
       "\n",
       "            timestamp  a_user_id  a_follower_count  a_following_count  \\\n",
       "146255999  1582081341    1362744              5390                833   \n",
       "146256000  1582091134     179475             17747               1467   \n",
       "146256001  1582086464     366281              4386                 80   \n",
       "146256002  1581665518    1030299              4236               4119   \n",
       "146256003  1581799075    6937005              4354               3629   \n",
       "\n",
       "           a_is_verified  a_account_creation  b_user_id  b_follower_count  \\\n",
       "146255999              0          1546658592   30848700              5390   \n",
       "146256000              0          1172558525    4950585             17747   \n",
       "146256001              0          1457176980   30312854              4386   \n",
       "146256002              0          1524226898    3141261              4354   \n",
       "146256003              0          1269050894    3141261              4354   \n",
       "\n",
       "           b_following_count  b_is_verified  b_account_creation  b_follows_a  \\\n",
       "146255999                833              0          1428007228            0   \n",
       "146256000               1467              0          1272381192            0   \n",
       "146256001                 80              0          1235182992            0   \n",
       "146256002               3629              0          1501554925            1   \n",
       "146256003               3629              0          1501554925            1   \n",
       "\n",
       "           reply  retweet  retweet_comment  like         id  tr  dt_day  \\\n",
       "146255999      0        0                0     0  146255999   2      19   \n",
       "146256000      0        0                0     0  146256000   2      19   \n",
       "146256001      0        0                0     0  146256001   2      19   \n",
       "146256002      0        0                0     0  146256002   2      14   \n",
       "146256003      0        0                0     0  146256003   2      15   \n",
       "\n",
       "           dt_dow  dt_hour  a_count_combined  a_user_fer_count_delta_time  \\\n",
       "146255999       2        3                 2                            1   \n",
       "146256000       2        5                 0                            0   \n",
       "146256001       2        4                 2                            1   \n",
       "146256002       4        7                 2                            1   \n",
       "146256003       5       20                 2                            1   \n",
       "\n",
       "           a_user_fing_count_delta_time  a_user_fering_count_delta_time  \\\n",
       "146255999                             1                               1   \n",
       "146256000                             1                               0   \n",
       "146256001                             1                               1   \n",
       "146256002                             1                               1   \n",
       "146256003                             1                               1   \n",
       "\n",
       "           a_user_fing_count_mode  a_user_fer_count_mode  \\\n",
       "146255999                       1                      1   \n",
       "146256000                       1                      0   \n",
       "146256001                       1                      1   \n",
       "146256002                       1                      1   \n",
       "146256003                       1                      1   \n",
       "\n",
       "           a_user_fering_count_mode  count_ats  count_char  count_words  \\\n",
       "146255999                         1          0          76           15   \n",
       "146256000                         0          0         159           29   \n",
       "146256001                         1          0          72           10   \n",
       "146256002                         1          0         161           42   \n",
       "146256003                         1          0         216           71   \n",
       "\n",
       "            tw_hash  tw_freq_hash  tw_first_word  tw_second_word  \\\n",
       "146255999  39170347      34858164          28651          210319   \n",
       "146256000  38847520       1535005         300662          267542   \n",
       "146256001  49748666      44369960        7361311          466019   \n",
       "146256002  50323632      44880036        7551233          985413   \n",
       "146256003  50323633      44880037        2674107         2769099   \n",
       "\n",
       "           tw_last_word  tw_llast_word  tw_len  tw_hash0  tw_hash1  \\\n",
       "146255999          2793          27536       6         0         0   \n",
       "146256000            11            867      13         0         0   \n",
       "146256001         19261         202536       5         0         0   \n",
       "146256002           112           2712      14         0         0   \n",
       "146256003           646           2378      26         0         0   \n",
       "\n",
       "           tw_rt_uhash  TE_b_user_id_tweet_type_language_retweet_comment  \\\n",
       "146255999         7739                                               NaN   \n",
       "146256000        10748                                          0.006621   \n",
       "146256001        58462                                               NaN   \n",
       "146256002            0                                          0.002388   \n",
       "146256003            0                                          0.002388   \n",
       "\n",
       "           TE_tw_first_word_tweet_type_language_retweet_comment  \\\n",
       "146255999                                           0.014166      \n",
       "146256000                                           0.009727      \n",
       "146256001                                           0.006936      \n",
       "146256002                                                NaN      \n",
       "146256003                                           0.005023      \n",
       "\n",
       "           TE_tw_last_word_tweet_type_language_retweet_comment  \\\n",
       "146255999                                           0.012681     \n",
       "146256000                                           0.005502     \n",
       "146256001                                           0.000225     \n",
       "146256002                                           0.005743     \n",
       "146256003                                           0.003427     \n",
       "\n",
       "           TE_tw_hash0_tweet_type_language_retweet_comment  \\\n",
       "146255999                                         0.010811   \n",
       "146256000                                         0.007278   \n",
       "146256001                                         0.006057   \n",
       "146256002                                         0.003372   \n",
       "146256003                                         0.003372   \n",
       "\n",
       "           TE_tw_hash1_tweet_type_language_retweet_comment  \\\n",
       "146255999                                         0.010302   \n",
       "146256000                                         0.006833   \n",
       "146256001                                         0.005832   \n",
       "146256002                                         0.003387   \n",
       "146256003                                         0.003387   \n",
       "\n",
       "           TE_tw_rt_uhash_tweet_type_language_retweet_comment  \\\n",
       "146255999                                           0.013181    \n",
       "146256000                                           0.008766    \n",
       "146256001                                           0.000792    \n",
       "146256002                                           0.003372    \n",
       "146256003                                           0.003372    \n",
       "\n",
       "           TE_a_user_id_retweet_comment  TE_b_user_id_retweet_comment  \\\n",
       "146255999                      0.002697                           NaN   \n",
       "146256000                      0.001267                      0.004855   \n",
       "146256001                      0.004092                           NaN   \n",
       "146256002                      0.001995                      0.002081   \n",
       "146256003                      0.005827                      0.002081   \n",
       "\n",
       "           TE_tw_hash_retweet_comment  TE_tw_freq_hash_retweet_comment  \\\n",
       "146255999                         NaN                              NaN   \n",
       "146256000                         NaN                         0.006936   \n",
       "146256001                         NaN                              NaN   \n",
       "146256002                         NaN                              NaN   \n",
       "146256003                         NaN                              NaN   \n",
       "\n",
       "           TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet_comment  \\\n",
       "146255999                                           0.006404                                      \n",
       "146256000                                           0.005237                                      \n",
       "146256001                                           0.005116                                      \n",
       "146256002                                           0.002645                                      \n",
       "146256003                                           0.003437                                      \n",
       "\n",
       "           TE_a_count_combined_tweet_type_language_retweet_comment  \\\n",
       "146255999                                           0.023603         \n",
       "146256000                                           0.000053         \n",
       "146256001                                           0.011182         \n",
       "146256002                                           0.005460         \n",
       "146256003                                           0.005460         \n",
       "\n",
       "           TE_a_user_fer_count_delta_time_media_language_retweet_comment  \\\n",
       "146255999                                           0.017441               \n",
       "146256000                                           0.000177               \n",
       "146256001                                           0.009863               \n",
       "146256002                                           0.006452               \n",
       "146256003                                           0.005522               \n",
       "\n",
       "           TE_a_user_fing_count_delta_time_media_language_retweet_comment  \\\n",
       "146255999                                           0.014932                \n",
       "146256000                                           0.011367                \n",
       "146256001                                           0.008235                \n",
       "146256002                                           0.004916                \n",
       "146256003                                           0.004699                \n",
       "\n",
       "           TE_a_user_fering_count_delta_time_tweet_type_language_retweet_comment  \\\n",
       "146255999                                           0.023445                       \n",
       "146256000                                           0.000117                       \n",
       "146256001                                           0.011110                       \n",
       "146256002                                           0.005489                       \n",
       "146256003                                           0.005489                       \n",
       "\n",
       "           TE_a_user_fing_count_mode_media_language_retweet_comment  \\\n",
       "146255999                                           0.014341          \n",
       "146256000                                           0.010685          \n",
       "146256001                                           0.008121          \n",
       "146256002                                           0.004650          \n",
       "146256003                                           0.004540          \n",
       "\n",
       "           TE_a_user_fer_count_mode_media_language_retweet_comment  \\\n",
       "146255999                                           0.016752         \n",
       "146256000                                           0.000330         \n",
       "146256001                                           0.009712         \n",
       "146256002                                           0.006107         \n",
       "146256003                                           0.005375         \n",
       "\n",
       "           TE_a_user_fering_count_mode_tweet_type_language_retweet_comment  \\\n",
       "146255999                                           0.021614                 \n",
       "146256000                                           0.000245                 \n",
       "146256001                                           0.010607                 \n",
       "146256002                                           0.005307                 \n",
       "146256003                                           0.005307                 \n",
       "\n",
       "           TE_domains_media_tweet_type_language_retweet_comment  \\\n",
       "146255999                                           0.008811      \n",
       "146256000                                           0.006263      \n",
       "146256001                                           0.006457      \n",
       "146256002                                           0.002163      \n",
       "146256003                                           0.003575      \n",
       "\n",
       "           TE_links_media_tweet_type_language_retweet_comment  \\\n",
       "146255999                                           0.008811    \n",
       "146256000                                           0.006263    \n",
       "146256001                                           0.006457    \n",
       "146256002                                           0.002163    \n",
       "146256003                                           0.003575    \n",
       "\n",
       "           TE_hashtags_media_tweet_type_language_retweet_comment  \n",
       "146255999                                           0.008885      \n",
       "146256000                                           0.006419      \n",
       "146256001                                           0.006883      \n",
       "146256002                                                NaN      \n",
       "146256003                                           0.000089      "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60714530, 69), (60671901, 69), (12434735, 69), (12434838, 69))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(2020)\n",
    "SEED = 1\n",
    "\n",
    "valid = train.loc[ ((train.tweet_id % 2)==1)&(train.tr==0) ]\n",
    "gc.collect()\n",
    "\n",
    "test0 = train.loc[ (train.tr==1) ]\n",
    "gc.collect()\n",
    "\n",
    "test1 = train.loc[ (train.tr==2) ]\n",
    "gc.collect()\n",
    "\n",
    "train = train.loc[ ((train.tweet_id % 2)==0)&(train.tr==0) ]\n",
    "gc.collect()\n",
    "\n",
    "train.shape, valid.shape, test0.shape, test1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGB Version 1.1.0\n"
     ]
    }
   ],
   "source": [
    "xgb_parms = { \n",
    "    'max_depth':8, \n",
    "    'learning_rate':0.025, \n",
    "    'subsample':0.85,\n",
    "    'colsample_bytree':0.35, \n",
    "    'eval_metric':'logloss',\n",
    "    'objective':'binary:logistic',\n",
    "    'tree_method':'gpu_hist',\n",
    "    #'predictor': 'gpu_predictor',\n",
    "    'seed': 1,\n",
    "}\n",
    "\n",
    "import xgboost as xgb\n",
    "print('XGB Version',xgb.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CREATE TRAIN AND VALIDATION SETS\n",
    "RMV = [c for c in DONT_USE if c in train.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#########################\n",
      "### retweet_comment\n",
      "#########################\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#LEarning rates for 'reply', 'retweet', 'retweet_comment', 'like'\n",
    "LR = [0.05,0.03,0.07,0.01]\n",
    "\n",
    "#Like\n",
    "xgb_parms['learning_rate'] = LR[TARGET_id]\n",
    "\n",
    "print('#'*25);print('###',TARGET);print('#'*25)\n",
    "\n",
    "dtrain = xgb.DMatrix(data=train.drop(RMV, axis=1) ,label=train[TARGET].values)\n",
    "gc.collect()\n",
    "\n",
    "model = xgb.train(xgb_parms, \n",
    "                  dtrain=dtrain,\n",
    "                  num_boost_round=500,\n",
    "                 ) \n",
    "\n",
    "del dtrain\n",
    "gc.collect()  \n",
    "\n",
    "#save model\n",
    "joblib.dump(model, 'model-'+TARGET+'-1.xgb' ) \n",
    "del model\n",
    "gc.collect()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtrain = xgb.DMatrix(data=valid.drop(RMV, axis=1) ,label=valid[TARGET].values)\n",
    "gc.collect()\n",
    "\n",
    "model = xgb.train(xgb_parms, \n",
    "                  dtrain=dtrain,\n",
    "                  num_boost_round=500,\n",
    "                 ) \n",
    "\n",
    "del dtrain\n",
    "gc.collect()  \n",
    "\n",
    "#save model\n",
    "joblib.dump(model, 'model-'+TARGET+'-2.xgb' ) \n",
    "\n",
    "del model\n",
    "gc.collect()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load xgb1\n"
     ]
    }
   ],
   "source": [
    "print('load xgb1')\n",
    "model = joblib.load( 'model-'+TARGET+'-1.xgb' )\n",
    "dvalid = xgb.DMatrix(data=valid.drop(RMV, axis=1))\n",
    "valid['ypred'] = model.predict(dvalid)\n",
    "del dvalid, model; _=gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load xgb2\n"
     ]
    }
   ],
   "source": [
    "print('load xgb2')\n",
    "model = joblib.load( 'model-'+TARGET+'-2.xgb' )\n",
    "dtrain = xgb.DMatrix(data=train.drop(RMV, axis=1))\n",
    "train['ypred'] = model.predict(dtrain)\n",
    "del dtrain, model; _=gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OOF RCE:\n",
      "16.331572587956256\n",
      "16.422846769038934\n"
     ]
    }
   ],
   "source": [
    "print(\"OOF RCE:\")\n",
    "print( compute_rce( valid[TARGET].values, valid['ypred'].values ) )\n",
    "print( compute_rce( train[TARGET].values, train['ypred'].values ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ee1a01dd518>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt0 = train.groupby( ['dt_day','dt_hour'] )['ypred'].agg('mean').reset_index()\n",
    "dt1 = valid.groupby( ['dt_day','dt_hour'] )['ypred'].agg('mean').reset_index()\n",
    "dt0.sort_values(['dt_day','dt_hour'], inplace=True)\n",
    "dt1.sort_values(['dt_day','dt_hour'], inplace=True)\n",
    "\n",
    "dt0['ypred2'] = dt1['ypred'].values\n",
    "\n",
    "dt0[['ypred','ypred2']].plot(  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ee1a01d43c8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import plot_importance\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (12,12)\n",
    "\n",
    "model = joblib.load( 'model-'+TARGET+'-1.xgb' )\n",
    "plot_importance(model, height=1.0, show_values=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Public val.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "test0.sort_values( 'id', inplace=True )\n",
    "sub = pd.read_csv('../preprocessings/sample_submission_public.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hashtags</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>media</th>\n",
       "      <th>links</th>\n",
       "      <th>domains</th>\n",
       "      <th>tweet_type</th>\n",
       "      <th>language</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>a_user_id</th>\n",
       "      <th>a_follower_count</th>\n",
       "      <th>a_following_count</th>\n",
       "      <th>a_is_verified</th>\n",
       "      <th>a_account_creation</th>\n",
       "      <th>b_user_id</th>\n",
       "      <th>b_follower_count</th>\n",
       "      <th>b_following_count</th>\n",
       "      <th>b_is_verified</th>\n",
       "      <th>b_account_creation</th>\n",
       "      <th>b_follows_a</th>\n",
       "      <th>reply</th>\n",
       "      <th>retweet</th>\n",
       "      <th>retweet_comment</th>\n",
       "      <th>like</th>\n",
       "      <th>id</th>\n",
       "      <th>tr</th>\n",
       "      <th>dt_day</th>\n",
       "      <th>dt_dow</th>\n",
       "      <th>dt_hour</th>\n",
       "      <th>a_count_combined</th>\n",
       "      <th>a_user_fer_count_delta_time</th>\n",
       "      <th>a_user_fing_count_delta_time</th>\n",
       "      <th>a_user_fering_count_delta_time</th>\n",
       "      <th>a_user_fing_count_mode</th>\n",
       "      <th>a_user_fer_count_mode</th>\n",
       "      <th>a_user_fering_count_mode</th>\n",
       "      <th>count_ats</th>\n",
       "      <th>count_char</th>\n",
       "      <th>count_words</th>\n",
       "      <th>tw_hash</th>\n",
       "      <th>tw_freq_hash</th>\n",
       "      <th>tw_first_word</th>\n",
       "      <th>tw_second_word</th>\n",
       "      <th>tw_last_word</th>\n",
       "      <th>tw_llast_word</th>\n",
       "      <th>tw_len</th>\n",
       "      <th>tw_hash0</th>\n",
       "      <th>tw_hash1</th>\n",
       "      <th>tw_rt_uhash</th>\n",
       "      <th>TE_b_user_id_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_first_word_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_last_word_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_hash0_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_hash1_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_rt_uhash_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_a_user_id_retweet_comment</th>\n",
       "      <th>TE_b_user_id_retweet_comment</th>\n",
       "      <th>TE_tw_hash_retweet_comment</th>\n",
       "      <th>TE_tw_freq_hash_retweet_comment</th>\n",
       "      <th>TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet_comment</th>\n",
       "      <th>TE_a_count_combined_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fer_count_delta_time_media_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fing_count_delta_time_media_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fering_count_delta_time_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fing_count_mode_media_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fer_count_mode_media_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fering_count_mode_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_domains_media_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_links_media_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_hashtags_media_tweet_type_language_retweet_comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121386431</th>\n",
       "      <td>0</td>\n",
       "      <td>57733249</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>1581703126</td>\n",
       "      <td>534117</td>\n",
       "      <td>13919</td>\n",
       "      <td>1214</td>\n",
       "      <td>0</td>\n",
       "      <td>1448292186</td>\n",
       "      <td>3617447</td>\n",
       "      <td>8794</td>\n",
       "      <td>2134</td>\n",
       "      <td>0</td>\n",
       "      <td>1520948869</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121386431</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>5</td>\n",
       "      <td>38691181</td>\n",
       "      <td>34413698</td>\n",
       "      <td>562265</td>\n",
       "      <td>296463</td>\n",
       "      <td>83986</td>\n",
       "      <td>110811</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005602</td>\n",
       "      <td>0.045827</td>\n",
       "      <td>0.009460</td>\n",
       "      <td>0.009205</td>\n",
       "      <td>0.009460</td>\n",
       "      <td>0.002241</td>\n",
       "      <td>0.006069</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.014349</td>\n",
       "      <td>0.022257</td>\n",
       "      <td>0.013476</td>\n",
       "      <td>0.009600</td>\n",
       "      <td>0.015653</td>\n",
       "      <td>0.001049</td>\n",
       "      <td>0.012758</td>\n",
       "      <td>0.015018</td>\n",
       "      <td>0.008175</td>\n",
       "      <td>0.008175</td>\n",
       "      <td>0.007589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121386432</th>\n",
       "      <td>0</td>\n",
       "      <td>57733250</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>1582021842</td>\n",
       "      <td>2721240</td>\n",
       "      <td>186</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1263078566</td>\n",
       "      <td>12365145</td>\n",
       "      <td>111835</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1335110299</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121386432</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>5</td>\n",
       "      <td>38691182</td>\n",
       "      <td>34413699</td>\n",
       "      <td>7376000</td>\n",
       "      <td>4871859</td>\n",
       "      <td>348771</td>\n",
       "      <td>1398132</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>102048</td>\n",
       "      <td>0.006936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004699</td>\n",
       "      <td>0.005733</td>\n",
       "      <td>0.005475</td>\n",
       "      <td>0.004699</td>\n",
       "      <td>0.006621</td>\n",
       "      <td>0.004552</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004242</td>\n",
       "      <td>0.005181</td>\n",
       "      <td>0.009082</td>\n",
       "      <td>0.009082</td>\n",
       "      <td>0.005181</td>\n",
       "      <td>0.009082</td>\n",
       "      <td>0.009082</td>\n",
       "      <td>0.005181</td>\n",
       "      <td>0.005872</td>\n",
       "      <td>0.005872</td>\n",
       "      <td>0.006435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121386433</th>\n",
       "      <td>0</td>\n",
       "      <td>57733251</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>1581734918</td>\n",
       "      <td>2023199</td>\n",
       "      <td>249849</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1356488269</td>\n",
       "      <td>28952089</td>\n",
       "      <td>249849</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1503940711</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121386433</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>5</td>\n",
       "      <td>38691183</td>\n",
       "      <td>18372275</td>\n",
       "      <td>16591</td>\n",
       "      <td>29975</td>\n",
       "      <td>1205</td>\n",
       "      <td>23578</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005395</td>\n",
       "      <td>0.002428</td>\n",
       "      <td>0.017320</td>\n",
       "      <td>0.017281</td>\n",
       "      <td>0.017321</td>\n",
       "      <td>0.002913</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006936</td>\n",
       "      <td>0.011386</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.016925</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>0.016861</td>\n",
       "      <td>0.000865</td>\n",
       "      <td>0.000814</td>\n",
       "      <td>0.016351</td>\n",
       "      <td>0.016351</td>\n",
       "      <td>0.015176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121386434</th>\n",
       "      <td>0</td>\n",
       "      <td>57733252</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>1581913613</td>\n",
       "      <td>2816974</td>\n",
       "      <td>517</td>\n",
       "      <td>407</td>\n",
       "      <td>0</td>\n",
       "      <td>1449096567</td>\n",
       "      <td>13774342</td>\n",
       "      <td>574475</td>\n",
       "      <td>2656</td>\n",
       "      <td>0</td>\n",
       "      <td>1396311956</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121386434</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
       "      <td>16</td>\n",
       "      <td>38691184</td>\n",
       "      <td>34413700</td>\n",
       "      <td>7376001</td>\n",
       "      <td>165976</td>\n",
       "      <td>4845</td>\n",
       "      <td>16174</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2446</td>\n",
       "      <td>0.005202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004905</td>\n",
       "      <td>0.007278</td>\n",
       "      <td>0.006833</td>\n",
       "      <td>0.005793</td>\n",
       "      <td>0.004552</td>\n",
       "      <td>0.004552</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010166</td>\n",
       "      <td>0.015822</td>\n",
       "      <td>0.014914</td>\n",
       "      <td>0.012654</td>\n",
       "      <td>0.015690</td>\n",
       "      <td>0.012364</td>\n",
       "      <td>0.014579</td>\n",
       "      <td>0.014333</td>\n",
       "      <td>0.007491</td>\n",
       "      <td>0.007491</td>\n",
       "      <td>0.007829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121386435</th>\n",
       "      <td>0</td>\n",
       "      <td>57733253</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>1581565745</td>\n",
       "      <td>366629</td>\n",
       "      <td>19578</td>\n",
       "      <td>273</td>\n",
       "      <td>1</td>\n",
       "      <td>1236181798</td>\n",
       "      <td>11208153</td>\n",
       "      <td>218811</td>\n",
       "      <td>4980</td>\n",
       "      <td>0</td>\n",
       "      <td>1298646801</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121386435</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>10</td>\n",
       "      <td>38691185</td>\n",
       "      <td>34413701</td>\n",
       "      <td>4769376</td>\n",
       "      <td>56375</td>\n",
       "      <td>852</td>\n",
       "      <td>51742</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005395</td>\n",
       "      <td>0.006997</td>\n",
       "      <td>0.009460</td>\n",
       "      <td>0.009205</td>\n",
       "      <td>0.009460</td>\n",
       "      <td>0.000828</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006056</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000109</td>\n",
       "      <td>0.009600</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.009013</td>\n",
       "      <td>0.000376</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>0.008175</td>\n",
       "      <td>0.008175</td>\n",
       "      <td>0.007589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hashtags  tweet_id  media  links  domains  tweet_type  language  \\\n",
       "121386431         0  57733249      5      0        0           2        54   \n",
       "121386432         0  57733250      7      0        0           1        47   \n",
       "121386433         0  57733251      1      0        0           2        13   \n",
       "121386434         0  57733252      7      0        0           1        54   \n",
       "121386435         0  57733253      5      0        0           2        54   \n",
       "\n",
       "            timestamp  a_user_id  a_follower_count  a_following_count  \\\n",
       "121386431  1581703126     534117             13919               1214   \n",
       "121386432  1582021842    2721240               186                100   \n",
       "121386433  1581734918    2023199            249849                  1   \n",
       "121386434  1581913613    2816974               517                407   \n",
       "121386435  1581565745     366629             19578                273   \n",
       "\n",
       "           a_is_verified  a_account_creation  b_user_id  b_follower_count  \\\n",
       "121386431              0          1448292186    3617447              8794   \n",
       "121386432              0          1263078566   12365145            111835   \n",
       "121386433              0          1356488269   28952089            249849   \n",
       "121386434              0          1449096567   13774342            574475   \n",
       "121386435              1          1236181798   11208153            218811   \n",
       "\n",
       "           b_following_count  b_is_verified  b_account_creation  b_follows_a  \\\n",
       "121386431               2134              0          1520948869            1   \n",
       "121386432                  3              0          1335110299            0   \n",
       "121386433                  1              0          1503940711            0   \n",
       "121386434               2656              0          1396311956            1   \n",
       "121386435               4980              0          1298646801            0   \n",
       "\n",
       "           reply  retweet  retweet_comment  like         id  tr  dt_day  \\\n",
       "121386431      0        0                0     0  121386431   1      14   \n",
       "121386432      0        0                0     0  121386432   1      18   \n",
       "121386433      0        0                0     0  121386433   1      15   \n",
       "121386434      0        0                0     0  121386434   1      17   \n",
       "121386435      0        0                0     0  121386435   1      13   \n",
       "\n",
       "           dt_dow  dt_hour  a_count_combined  a_user_fer_count_delta_time  \\\n",
       "121386431       4       17                 6                            1   \n",
       "121386432       1       10                -9                           -9   \n",
       "121386433       5        2                 0                            0   \n",
       "121386434       0        4                 2                            1   \n",
       "121386435       3        3                 0                            0   \n",
       "\n",
       "           a_user_fing_count_delta_time  a_user_fering_count_delta_time  \\\n",
       "121386431                             1                               1   \n",
       "121386432                            -9                              -9   \n",
       "121386433                             1                               0   \n",
       "121386434                             1                               1   \n",
       "121386435                             1                               0   \n",
       "\n",
       "           a_user_fing_count_mode  a_user_fer_count_mode  \\\n",
       "121386431                       0                      1   \n",
       "121386432                      -9                     -9   \n",
       "121386433                       1                      0   \n",
       "121386434                       1                      1   \n",
       "121386435                       1                      0   \n",
       "\n",
       "           a_user_fering_count_mode  count_ats  count_char  count_words  \\\n",
       "121386431                         1          0          55            5   \n",
       "121386432                        -9          0          57            5   \n",
       "121386433                         0          0          49            5   \n",
       "121386434                         1          0         104           16   \n",
       "121386435                         0          0          82           10   \n",
       "\n",
       "            tw_hash  tw_freq_hash  tw_first_word  tw_second_word  \\\n",
       "121386431  38691181      34413698         562265          296463   \n",
       "121386432  38691182      34413699        7376000         4871859   \n",
       "121386433  38691183      18372275          16591           29975   \n",
       "121386434  38691184      34413700        7376001          165976   \n",
       "121386435  38691185      34413701        4769376           56375   \n",
       "\n",
       "           tw_last_word  tw_llast_word  tw_len  tw_hash0  tw_hash1  \\\n",
       "121386431         83986         110811       3         0         0   \n",
       "121386432        348771        1398132       2         0         0   \n",
       "121386433          1205          23578       2         0         0   \n",
       "121386434          4845          16174       7         0         0   \n",
       "121386435           852          51742       3         0         0   \n",
       "\n",
       "           tw_rt_uhash  TE_b_user_id_tweet_type_language_retweet_comment  \\\n",
       "121386431            0                                               NaN   \n",
       "121386432       102048                                          0.006936   \n",
       "121386433            0                                               NaN   \n",
       "121386434         2446                                          0.005202   \n",
       "121386435            0                                               NaN   \n",
       "\n",
       "           TE_tw_first_word_tweet_type_language_retweet_comment  \\\n",
       "121386431                                           0.005602      \n",
       "121386432                                                NaN      \n",
       "121386433                                           0.005395      \n",
       "121386434                                                NaN      \n",
       "121386435                                           0.005395      \n",
       "\n",
       "           TE_tw_last_word_tweet_type_language_retweet_comment  \\\n",
       "121386431                                           0.045827     \n",
       "121386432                                           0.004699     \n",
       "121386433                                           0.002428     \n",
       "121386434                                           0.004905     \n",
       "121386435                                           0.006997     \n",
       "\n",
       "           TE_tw_hash0_tweet_type_language_retweet_comment  \\\n",
       "121386431                                         0.009460   \n",
       "121386432                                         0.005733   \n",
       "121386433                                         0.017320   \n",
       "121386434                                         0.007278   \n",
       "121386435                                         0.009460   \n",
       "\n",
       "           TE_tw_hash1_tweet_type_language_retweet_comment  \\\n",
       "121386431                                         0.009205   \n",
       "121386432                                         0.005475   \n",
       "121386433                                         0.017281   \n",
       "121386434                                         0.006833   \n",
       "121386435                                         0.009205   \n",
       "\n",
       "           TE_tw_rt_uhash_tweet_type_language_retweet_comment  \\\n",
       "121386431                                           0.009460    \n",
       "121386432                                           0.004699    \n",
       "121386433                                           0.017321    \n",
       "121386434                                           0.005793    \n",
       "121386435                                           0.009460    \n",
       "\n",
       "           TE_a_user_id_retweet_comment  TE_b_user_id_retweet_comment  \\\n",
       "121386431                      0.002241                      0.006069   \n",
       "121386432                      0.006621                      0.004552   \n",
       "121386433                      0.002913                           NaN   \n",
       "121386434                      0.004552                      0.004552   \n",
       "121386435                      0.000828                           NaN   \n",
       "\n",
       "           TE_tw_hash_retweet_comment  TE_tw_freq_hash_retweet_comment  \\\n",
       "121386431                         NaN                              NaN   \n",
       "121386432                         NaN                              NaN   \n",
       "121386433                         NaN                         0.006936   \n",
       "121386434                         NaN                              NaN   \n",
       "121386435                         NaN                              NaN   \n",
       "\n",
       "           TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet_comment  \\\n",
       "121386431                                           0.014349                                      \n",
       "121386432                                           0.004242                                      \n",
       "121386433                                           0.011386                                      \n",
       "121386434                                           0.010166                                      \n",
       "121386435                                           0.006056                                      \n",
       "\n",
       "           TE_a_count_combined_tweet_type_language_retweet_comment  \\\n",
       "121386431                                           0.022257         \n",
       "121386432                                           0.005181         \n",
       "121386433                                           0.000090         \n",
       "121386434                                           0.015822         \n",
       "121386435                                           0.000027         \n",
       "\n",
       "           TE_a_user_fer_count_delta_time_media_language_retweet_comment  \\\n",
       "121386431                                           0.013476               \n",
       "121386432                                           0.009082               \n",
       "121386433                                           0.000108               \n",
       "121386434                                           0.014914               \n",
       "121386435                                           0.000109               \n",
       "\n",
       "           TE_a_user_fing_count_delta_time_media_language_retweet_comment  \\\n",
       "121386431                                           0.009600                \n",
       "121386432                                           0.009082                \n",
       "121386433                                           0.016925                \n",
       "121386434                                           0.012654                \n",
       "121386435                                           0.009600                \n",
       "\n",
       "           TE_a_user_fering_count_delta_time_tweet_type_language_retweet_comment  \\\n",
       "121386431                                           0.015653                       \n",
       "121386432                                           0.005181                       \n",
       "121386433                                           0.000479                       \n",
       "121386434                                           0.015690                       \n",
       "121386435                                           0.000096                       \n",
       "\n",
       "           TE_a_user_fing_count_mode_media_language_retweet_comment  \\\n",
       "121386431                                           0.001049          \n",
       "121386432                                           0.009082          \n",
       "121386433                                           0.016861          \n",
       "121386434                                           0.012364          \n",
       "121386435                                           0.009013          \n",
       "\n",
       "           TE_a_user_fer_count_mode_media_language_retweet_comment  \\\n",
       "121386431                                           0.012758         \n",
       "121386432                                           0.009082         \n",
       "121386433                                           0.000865         \n",
       "121386434                                           0.014579         \n",
       "121386435                                           0.000376         \n",
       "\n",
       "           TE_a_user_fering_count_mode_tweet_type_language_retweet_comment  \\\n",
       "121386431                                           0.015018                 \n",
       "121386432                                           0.005181                 \n",
       "121386433                                           0.000814                 \n",
       "121386434                                           0.014333                 \n",
       "121386435                                           0.000412                 \n",
       "\n",
       "           TE_domains_media_tweet_type_language_retweet_comment  \\\n",
       "121386431                                           0.008175      \n",
       "121386432                                           0.005872      \n",
       "121386433                                           0.016351      \n",
       "121386434                                           0.007491      \n",
       "121386435                                           0.008175      \n",
       "\n",
       "           TE_links_media_tweet_type_language_retweet_comment  \\\n",
       "121386431                                           0.008175    \n",
       "121386432                                           0.005872    \n",
       "121386433                                           0.016351    \n",
       "121386434                                           0.007491    \n",
       "121386435                                           0.008175    \n",
       "\n",
       "           TE_hashtags_media_tweet_type_language_retweet_comment  \n",
       "121386431                                           0.007589      \n",
       "121386432                                           0.006435      \n",
       "121386433                                           0.015176      \n",
       "121386434                                           0.007829      \n",
       "121386435                                           0.007589      "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtest = xgb.DMatrix(data=test0.drop(RMV, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12434735"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = joblib.load( 'model-'+TARGET+'-1.xgb')\n",
    "ytest = model.predict(dtest,ntree_limit=500)\n",
    "del model; gc.collect()\n",
    "len(ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12434735"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = joblib.load( 'model-'+TARGET+'-2.xgb' )\n",
    "ytest += model.predict(dtest)\n",
    "ytest /= 2.\n",
    "del model, dtest; gc.collect()\n",
    "len(ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dt_day\n",
      "13    0.006469\n",
      "14    0.006112\n",
      "15    0.006786\n",
      "16    0.007195\n",
      "17    0.007985\n",
      "18    0.008246\n",
      "19    0.006452\n",
      "Name: ypred, dtype: float32\n",
      "0.007979182 0.00893369 0.008308827 0.007552125 0.8931563 0.908928 0.9010421101476129\n"
     ]
    }
   ],
   "source": [
    "test0['ypred'] = ytest\n",
    "\n",
    "print( test0.groupby('dt_day')['ypred'].mean() )\n",
    "m17 = test0.loc[ (test0.dt_day==17)&(test0.dt_hour==23), 'ypred' ].mean()\n",
    "m18a = test0.loc[ (test0.dt_day==18)&(test0.dt_hour==0), 'ypred' ].mean()\n",
    "m18b = test0.loc[ (test0.dt_day==18)&(test0.dt_hour==23), 'ypred' ].mean()\n",
    "m19 = test0.loc[ (test0.dt_day==19)&(test0.dt_hour==0), 'ypred' ].mean()\n",
    "\n",
    "print( m17, m18a, m18b, m19, m17/m18a, m19/m18b,0.5*m17/m18a+ 0.5*m19/m18b )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0069394694"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test0.loc[ test0.dt_day==18 ,'ypred'] *=  ( 0.5*m17/m18a+ 0.5*m19/m18b )\n",
    "\n",
    "sub['prediction'] = test0['ypred'].values\n",
    "sub.to_csv('submissions/xgb-final-'+TARGET+'-public-1.csv',header=None, index=False)\n",
    "sub['prediction'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Private competition_test.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1.sort_values( 'id', inplace=True )\n",
    "sub = pd.read_csv('../preprocessings/sample_submission_private.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hashtags</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>media</th>\n",
       "      <th>links</th>\n",
       "      <th>domains</th>\n",
       "      <th>tweet_type</th>\n",
       "      <th>language</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>a_user_id</th>\n",
       "      <th>a_follower_count</th>\n",
       "      <th>a_following_count</th>\n",
       "      <th>a_is_verified</th>\n",
       "      <th>a_account_creation</th>\n",
       "      <th>b_user_id</th>\n",
       "      <th>b_follower_count</th>\n",
       "      <th>b_following_count</th>\n",
       "      <th>b_is_verified</th>\n",
       "      <th>b_account_creation</th>\n",
       "      <th>b_follows_a</th>\n",
       "      <th>reply</th>\n",
       "      <th>retweet</th>\n",
       "      <th>retweet_comment</th>\n",
       "      <th>like</th>\n",
       "      <th>id</th>\n",
       "      <th>tr</th>\n",
       "      <th>dt_day</th>\n",
       "      <th>dt_dow</th>\n",
       "      <th>dt_hour</th>\n",
       "      <th>a_count_combined</th>\n",
       "      <th>a_user_fer_count_delta_time</th>\n",
       "      <th>a_user_fing_count_delta_time</th>\n",
       "      <th>a_user_fering_count_delta_time</th>\n",
       "      <th>a_user_fing_count_mode</th>\n",
       "      <th>a_user_fer_count_mode</th>\n",
       "      <th>a_user_fering_count_mode</th>\n",
       "      <th>count_ats</th>\n",
       "      <th>count_char</th>\n",
       "      <th>count_words</th>\n",
       "      <th>tw_hash</th>\n",
       "      <th>tw_freq_hash</th>\n",
       "      <th>tw_first_word</th>\n",
       "      <th>tw_second_word</th>\n",
       "      <th>tw_last_word</th>\n",
       "      <th>tw_llast_word</th>\n",
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       "      <th>tw_hash1</th>\n",
       "      <th>tw_rt_uhash</th>\n",
       "      <th>TE_b_user_id_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_first_word_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_last_word_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_hash0_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_hash1_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_tw_rt_uhash_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_a_user_id_retweet_comment</th>\n",
       "      <th>TE_b_user_id_retweet_comment</th>\n",
       "      <th>TE_tw_hash_retweet_comment</th>\n",
       "      <th>TE_tw_freq_hash_retweet_comment</th>\n",
       "      <th>TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet_comment</th>\n",
       "      <th>TE_a_count_combined_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fer_count_delta_time_media_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fing_count_delta_time_media_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fering_count_delta_time_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fing_count_mode_media_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fer_count_mode_media_language_retweet_comment</th>\n",
       "      <th>TE_a_user_fering_count_mode_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_domains_media_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_links_media_tweet_type_language_retweet_comment</th>\n",
       "      <th>TE_hashtags_media_tweet_type_language_retweet_comment</th>\n",
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       "      <td>0.006057</td>\n",
       "      <td>0.005832</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001324</td>\n",
       "      <td>0.004284</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.007765</td>\n",
       "      <td>0.011182</td>\n",
       "      <td>0.011107</td>\n",
       "      <td>0.008818</td>\n",
       "      <td>0.011110</td>\n",
       "      <td>0.008646</td>\n",
       "      <td>0.010763</td>\n",
       "      <td>0.010607</td>\n",
       "      <td>0.005332</td>\n",
       "      <td>0.005332</td>\n",
       "      <td>0.005602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133821170</th>\n",
       "      <td>518727</td>\n",
       "      <td>66410933</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>1581779241</td>\n",
       "      <td>4280276</td>\n",
       "      <td>1020</td>\n",
       "      <td>2097</td>\n",
       "      <td>0</td>\n",
       "      <td>1468438879</td>\n",
       "      <td>29849501</td>\n",
       "      <td>405774</td>\n",
       "      <td>974</td>\n",
       "      <td>0</td>\n",
       "      <td>1385502405</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>133821170</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>5</td>\n",
       "      <td>167</td>\n",
       "      <td>24</td>\n",
       "      <td>45410187</td>\n",
       "      <td>40509201</td>\n",
       "      <td>7773604</td>\n",
       "      <td>5355936</td>\n",
       "      <td>3030</td>\n",
       "      <td>2103</td>\n",
       "      <td>9</td>\n",
       "      <td>6521258</td>\n",
       "      <td>5953099</td>\n",
       "      <td>325751</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006333</td>\n",
       "      <td>0.006069</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.003904</td>\n",
       "      <td>0.003390</td>\n",
       "      <td>0.005118</td>\n",
       "      <td>0.007105</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>0.007596</td>\n",
       "      <td>0.006760</td>\n",
       "      <td>0.002317</td>\n",
       "      <td>0.005659</td>\n",
       "      <td>0.005659</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hashtags  tweet_id  media  links  domains  tweet_type  language  \\\n",
       "133821166         0  66410929      0      0        0           2        11   \n",
       "133821167     25339  66410930      0      0        0           1        11   \n",
       "133821168         0  66410931      9      0        0           1        54   \n",
       "133821169     26906  66410932      5      0        0           1         4   \n",
       "133821170    518727  66410933      5      0        0           1        54   \n",
       "\n",
       "            timestamp  a_user_id  a_follower_count  a_following_count  \\\n",
       "133821166  1581759640   12167358               119                125   \n",
       "133821167  1581668217    9371104               189                264   \n",
       "133821168  1582046459     199258              4312                660   \n",
       "133821169  1582083666     924614               272                185   \n",
       "133821170  1581779241    4280276              1020               2097   \n",
       "\n",
       "           a_is_verified  a_account_creation  b_user_id  b_follower_count  \\\n",
       "133821166              0          1571666822    6237935               189   \n",
       "133821167              0          1575966890    6237935               189   \n",
       "133821168              0          1494251627     879586              4312   \n",
       "133821169              0          1559086871     647103               272   \n",
       "133821170              0          1468438879   29849501            405774   \n",
       "\n",
       "           b_following_count  b_is_verified  b_account_creation  b_follows_a  \\\n",
       "133821166                264              0          1478011810            1   \n",
       "133821167                264              0          1478011810            1   \n",
       "133821168                660              0          1540395738            1   \n",
       "133821169                185              0          1432084055            1   \n",
       "133821170                974              0          1385502405            0   \n",
       "\n",
       "           reply  retweet  retweet_comment  like         id  tr  dt_day  \\\n",
       "133821166      0        0                0     0  133821166   2      15   \n",
       "133821167      0        0                0     0  133821167   2      14   \n",
       "133821168      0        0                0     0  133821168   2      18   \n",
       "133821169      0        0                0     0  133821169   2      19   \n",
       "133821170      0        0                0     0  133821170   2      15   \n",
       "\n",
       "           dt_dow  dt_hour  a_count_combined  a_user_fer_count_delta_time  \\\n",
       "133821166       5        9                 2                            1   \n",
       "133821167       4        8                 2                            1   \n",
       "133821168       1       17                -9                           -9   \n",
       "133821169       2        3                 2                            1   \n",
       "133821170       5       15                10                           -1   \n",
       "\n",
       "           a_user_fing_count_delta_time  a_user_fering_count_delta_time  \\\n",
       "133821166                             1                               1   \n",
       "133821167                             1                               1   \n",
       "133821168                            -9                              -9   \n",
       "133821169                             1                               1   \n",
       "133821170                            -1                              -1   \n",
       "\n",
       "           a_user_fing_count_mode  a_user_fer_count_mode  \\\n",
       "133821166                       1                      1   \n",
       "133821167                       1                      1   \n",
       "133821168                      -9                     -9   \n",
       "133821169                       1                      1   \n",
       "133821170                      -1                     -1   \n",
       "\n",
       "           a_user_fering_count_mode  count_ats  count_char  count_words  \\\n",
       "133821166                         1          0          59           15   \n",
       "133821167                         1          0         189           36   \n",
       "133821168                        -9          0         129           18   \n",
       "133821169                         1          0          73            9   \n",
       "133821170                        -1          5         167           24   \n",
       "\n",
       "            tw_hash  tw_freq_hash  tw_first_word  tw_second_word  \\\n",
       "133821166  45410185      40509199         163328           11867   \n",
       "133821167  43004406      38346818        4631037          288783   \n",
       "133821168  45410186      40509200        7627046         1151943   \n",
       "133821169  39837068      35471058        6610718         1008990   \n",
       "133821170  45410187      40509201        7773604         5355936   \n",
       "\n",
       "           tw_last_word  tw_llast_word  tw_len  tw_hash0  tw_hash1  \\\n",
       "133821166          5615           8175       5         0         0   \n",
       "133821167           808            902      11         0         0   \n",
       "133821168          2638          18369       8         0         0   \n",
       "133821169         69708          35969       3         0         0   \n",
       "133821170          3030           2103       9   6521258   5953099   \n",
       "\n",
       "           tw_rt_uhash  TE_b_user_id_tweet_type_language_retweet_comment  \\\n",
       "133821166            0                                          0.005395   \n",
       "133821167      3665864                                               NaN   \n",
       "133821168       139857                                          0.006621   \n",
       "133821169       364477                                               NaN   \n",
       "133821170       325751                                               NaN   \n",
       "\n",
       "           TE_tw_first_word_tweet_type_language_retweet_comment  \\\n",
       "133821166                                           0.003130      \n",
       "133821167                                           0.006333      \n",
       "133821168                                                NaN      \n",
       "133821169                                                NaN      \n",
       "133821170                                                NaN      \n",
       "\n",
       "           TE_tw_last_word_tweet_type_language_retweet_comment  \\\n",
       "133821166                                           0.002387     \n",
       "133821167                                           0.002068     \n",
       "133821168                                           0.001919     \n",
       "133821169                                           0.004284     \n",
       "133821170                                           0.005872     \n",
       "\n",
       "           TE_tw_hash0_tweet_type_language_retweet_comment  \\\n",
       "133821166                                         0.003372   \n",
       "133821167                                         0.003932   \n",
       "133821168                                         0.007278   \n",
       "133821169                                         0.006057   \n",
       "133821170                                              NaN   \n",
       "\n",
       "           TE_tw_hash1_tweet_type_language_retweet_comment  \\\n",
       "133821166                                         0.003387   \n",
       "133821167                                         0.003846   \n",
       "133821168                                         0.006833   \n",
       "133821169                                         0.005832   \n",
       "133821170                                              NaN   \n",
       "\n",
       "           TE_tw_rt_uhash_tweet_type_language_retweet_comment  \\\n",
       "133821166                                           0.003372    \n",
       "133821167                                                NaN    \n",
       "133821168                                           0.003237    \n",
       "133821169                                                NaN    \n",
       "133821170                                           0.006333    \n",
       "\n",
       "           TE_a_user_id_retweet_comment  TE_b_user_id_retweet_comment  \\\n",
       "133821166                      0.006621                      0.005202   \n",
       "133821167                      0.006333                      0.005202   \n",
       "133821168                      0.016379                      0.071522   \n",
       "133821169                      0.001324                      0.004284   \n",
       "133821170                      0.006069                           NaN   \n",
       "\n",
       "           TE_tw_hash_retweet_comment  TE_tw_freq_hash_retweet_comment  \\\n",
       "133821166                         NaN                              NaN   \n",
       "133821167                         NaN                              NaN   \n",
       "133821168                         NaN                              NaN   \n",
       "133821169                         NaN                              NaN   \n",
       "133821170                         NaN                              NaN   \n",
       "\n",
       "           TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet_comment  \\\n",
       "133821166                                           0.002645                                      \n",
       "133821167                                           0.005210                                      \n",
       "133821168                                           0.011751                                      \n",
       "133821169                                           0.007765                                      \n",
       "133821170                                           0.003904                                      \n",
       "\n",
       "           TE_a_count_combined_tweet_type_language_retweet_comment  \\\n",
       "133821166                                           0.005460         \n",
       "133821167                                           0.009145         \n",
       "133821168                                           0.006670         \n",
       "133821169                                           0.011182         \n",
       "133821170                                           0.003390         \n",
       "\n",
       "           TE_a_user_fer_count_delta_time_media_language_retweet_comment  \\\n",
       "133821166                                           0.006452               \n",
       "133821167                                           0.006452               \n",
       "133821168                                           0.011640               \n",
       "133821169                                           0.011107               \n",
       "133821170                                           0.005118               \n",
       "\n",
       "           TE_a_user_fing_count_delta_time_media_language_retweet_comment  \\\n",
       "133821166                                           0.004916                \n",
       "133821167                                           0.004916                \n",
       "133821168                                           0.011640                \n",
       "133821169                                           0.008818                \n",
       "133821170                                           0.007105                \n",
       "\n",
       "           TE_a_user_fering_count_delta_time_tweet_type_language_retweet_comment  \\\n",
       "133821166                                           0.005489                       \n",
       "133821167                                           0.009125                       \n",
       "133821168                                           0.006670                       \n",
       "133821169                                           0.011110                       \n",
       "133821170                                           0.002120                       \n",
       "\n",
       "           TE_a_user_fing_count_mode_media_language_retweet_comment  \\\n",
       "133821166                                           0.004650          \n",
       "133821167                                           0.004650          \n",
       "133821168                                           0.011640          \n",
       "133821169                                           0.008646          \n",
       "133821170                                           0.007596          \n",
       "\n",
       "           TE_a_user_fer_count_mode_media_language_retweet_comment  \\\n",
       "133821166                                           0.006107         \n",
       "133821167                                           0.006107         \n",
       "133821168                                           0.011640         \n",
       "133821169                                           0.010763         \n",
       "133821170                                           0.006760         \n",
       "\n",
       "           TE_a_user_fering_count_mode_tweet_type_language_retweet_comment  \\\n",
       "133821166                                           0.005307                 \n",
       "133821167                                           0.008451                 \n",
       "133821168                                           0.006670                 \n",
       "133821169                                           0.010607                 \n",
       "133821170                                           0.002317                 \n",
       "\n",
       "           TE_domains_media_tweet_type_language_retweet_comment  \\\n",
       "133821166                                           0.002163      \n",
       "133821167                                           0.003992      \n",
       "133821168                                           0.009492      \n",
       "133821169                                           0.005332      \n",
       "133821170                                           0.005659      \n",
       "\n",
       "           TE_links_media_tweet_type_language_retweet_comment  \\\n",
       "133821166                                           0.002163    \n",
       "133821167                                           0.003992    \n",
       "133821168                                           0.009492    \n",
       "133821169                                           0.005332    \n",
       "133821170                                           0.005659    \n",
       "\n",
       "           TE_hashtags_media_tweet_type_language_retweet_comment  \n",
       "133821166                                           0.002680      \n",
       "133821167                                           0.003388      \n",
       "133821168                                           0.009592      \n",
       "133821169                                           0.005602      \n",
       "133821170                                                NaN      "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtest = xgb.DMatrix(data=test1.drop(RMV, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12434838"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = joblib.load( 'model-'+TARGET+'-1.xgb' )\n",
    "ytest = model.predict(dtest)\n",
    "del model; gc.collect()\n",
    "len(ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12434838"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = joblib.load( 'model-'+TARGET+'-2.xgb' )\n",
    "ytest += model.predict(dtest)\n",
    "ytest /= 2.\n",
    "del model, dtest; gc.collect()\n",
    "len(ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dt_day\n",
      "13    0.006396\n",
      "14    0.006045\n",
      "15    0.006749\n",
      "16    0.007144\n",
      "17    0.007905\n",
      "18    0.008101\n",
      "19    0.006422\n",
      "Name: ypred, dtype: float32\n",
      "0.008088676 0.008785921 0.008239058 0.0076286364 0.92064065 0.92591125 0.9232759609540787\n"
     ]
    }
   ],
   "source": [
    "test1['ypred'] = ytest\n",
    "\n",
    "print( test1.groupby('dt_day')['ypred'].mean() )\n",
    "\n",
    "m17 = test1.loc[ (test1.dt_day==17)&(test1.dt_hour==23), 'ypred' ].mean()\n",
    "m18a = test1.loc[ (test1.dt_day==18)&(test1.dt_hour==0), 'ypred' ].mean()\n",
    "m18b = test1.loc[ (test1.dt_day==18)&(test1.dt_hour==23), 'ypred' ].mean()\n",
    "m19 = test1.loc[ (test1.dt_day==19)&(test1.dt_hour==0), 'ypred' ].mean()\n",
    "print( m17, m18a, m18b, m19, m17/m18a, m19/m18b, 0.5*m17/m18a+ 0.5*m19/m18b )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0068974122"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test1.loc[ test1.dt_day==18 ,'ypred'] *= ( 0.5*m17/m18a+ 0.5*m19/m18b )\n",
    "\n",
    "sub['prediction'] = test1['ypred'].values\n",
    "sub.to_csv('submissions/xgb-'+TARGET+'-private-1.csv',header=None, index=False)\n",
    "sub['prediction'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ee1a01da8d0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt0 = test0.groupby( ['dt_day','dt_hour'] )['ypred'].agg('mean').reset_index()\n",
    "dt1 = test1.groupby( ['dt_day','dt_hour'] )['ypred'].agg('mean').reset_index()\n",
    "dt0.sort_values(['dt_day','dt_hour'], inplace=True)\n",
    "dt1.sort_values(['dt_day','dt_hour'], inplace=True)\n",
    "\n",
    "dt0['ypred2'] = dt1['ypred'].values\n",
    "\n",
    "dt0[['ypred','ypred2']].plot(  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed Time is 41.092223 minutes\n"
     ]
    }
   ],
   "source": [
    "print('Elapsed Time is %f minutes'%((time.time()-startNB)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
